Computing personalized probabilistic familiarity based on known artifact data

ABSTRACT

Software that uses personalized information pertaining to a user to determine how familiar (or “novel” or “surprising”) a new artifact will be to the user, by performing the following steps: (i) receiving the identity of a first user; (ii) receiving a first dataset pertaining to the first user; (iii) receiving the identity of a first artifact; and (iv) applying a probabilistic familiarity algorithm to the first dataset with respect to the first artifact to yield a probabilistic familiarity value for the first artifact with respect to the first user. The first dataset is received over a computer network, and the first dataset includes at least one piece of personalized information for the first user.

STATEMENT ON PRIOR DISCLOSURES BY AN INVENTOR

The following disclosure(s) are submitted under 35 U.S.C. 102(b)(1)(A)as prior disclosures by, or on behalf of, a sole inventor of the presentapplication or a joint inventor of the present application:

(i) “Computational Creativity for Culinary Recipes”, Florian Pinel andLay R. Varshney, CHI 2014, Apr. 26-May 1, 2014, Toronto, Ontario,Canada, pages 439-442;

(ii) “Computational Creativity for Personalized Artifact Creation”, NanShao, Pavankumar Murali, and Anshul Sheopuri, INFORMS annual meeting,Wednesday, Nov. 12, 2014;

(iii) “New Developments in Culinary Computational Creativity”, Nan Shao,Pavankumar Murali, and Anshul Sheopuri, Fifth International Conferenceon Computational Creativity, Jun. 9-13, 2014; and

(iv) “Personalization of Product Novelty Assessment via BayesianSurprise” Nan Shao, Kush R. Varshney, Lay R. Varshney, and FlorianPinel, JSM 2014 Online Program, Aug. 3, 2014.

BACKGROUND OF THE INVENTION

The present invention relates generally to the field of measuringfamiliarity, and more particularly to measuring the probabilisticfamiliarity of artifacts.

Bayesian statistics is a known statistical field where information isexpressed in terms of degrees of belief or, more specifically,probabilities. In Bayesian statistics, a prior probability distributionrepresents the probability of certain events occurring before some newevidence is taken into account, and a posterior probability distributionrepresents the conditional probability of the same events occurringafter the new evidence is taken into account.

Bayesian surprise is known. Bayesian surprise is the quantification ofthe difference (or change) between a prior probability distribution anda corresponding posterior probability distribution. Bayesian surprisecan be used in machine learning to determine how novel (or surprising) anew item (or “artifact”) is given a known dataset of existing items. Oneway to quantify the difference between a prior probability distributionand a corresponding posterior probability distribution is by calculatinga Kullback-Leibler divergence, which is a measure of the informationgained by moving from the prior probability distribution to theposterior probability distribution.

SUMMARY

According to an aspect of the present invention, there is a method,computer program product, and/or system that performs the followingsteps (not necessarily in the following order): (i) receiving theidentity of a first user; (ii) receiving a first dataset pertaining tothe first user; (iii) receiving the identity of a first artifact; and(iv) applying a probabilistic familiarity algorithm to the first datasetwith respect to the first artifact to yield a probabilistic familiarityvalue for the first artifact with respect to the first user. The firstdataset is received over a computer network, and the first datasetincludes at least one piece of personalized information for the firstuser.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram view of a first embodiment of a systemaccording to the present invention;

FIG. 2 is a flow chart showing a first embodiment method performed, atleast in part, by the first embodiment system;

FIG. 3A is a block diagram view of a machine logic (for example,software) portion of the first embodiment system;

FIG. 3B is a block diagram that is helpful in understanding the firstembodiment system;

FIG. 3C is a block diagram that is helpful in understanding the firstembodiment system;

FIG. 4 is a flow chart showing a second embodiment method according to asecond embodiment system of the present invention;

FIG. 5 is a block diagram showing a machine logic (for example,software) portion of the second embodiment system;

FIG. 6 is a block diagram showing information that is helpful inunderstanding the second embodiment system;

FIG. 7 is a screenshot view generated by the second embodiment system;

FIG. 8A is a tree diagram showing information that is helpful inunderstanding the second embodiment system;

FIG. 8B is a tree diagram showing information that is helpful inunderstanding the second embodiment system;

FIG. 8C is a diagram showing information that is helpful inunderstanding the second embodiment system;

FIG. 9A is a tree diagram showing information that is helpful inunderstanding the second embodiment system;

FIG. 9B is a tree diagram showing information that is helpful inunderstanding the second embodiment system;

FIG. 9C is a tree diagram showing information that is helpful inunderstanding the second embodiment system;

FIG. 9D is a diagram showing information that is helpful inunderstanding the second embodiment system; and

FIG. 10 is a third embodiment method performed, at least in part, by thesecond embodiment system.

DETAILED DESCRIPTION

Embodiments of the present invention use personalized informationpertaining to a user to determine how familiar (or “novel” or“surprising”) a new artifact will be to the user. Personalizedinformation includes, for example, the user's activity on socialnetworking services, the user's online purchase history, and/or theuser's browser history. This Detailed Description section is dividedinto the following sub-sections: (i) The Hardware and SoftwareEnvironment; (ii) Example Embodiment; (iii) Further Comments and/orEmbodiments; and (v) Definitions.

I. The Hardware and Software Environment

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

An embodiment of a possible hardware and software environment forsoftware and/or methods according to the present invention will now bedescribed in detail with reference to the Figures. FIG. 1 is afunctional block diagram illustrating various portions of networkedcomputers system 100, including: server sub-system 102; clientsub-systems 104, 106, 108, 110, 112; communication network 114; servercomputer 200; communication unit 202; processor set 204; input/output(I/O) interface set 206; memory device 208; persistent storage device210; display device 212; external device set 214; random access memory(RAM) devices 230; cache memory device 232; and program 300.

Sub-system 102 is, in many respects, representative of the variouscomputer sub-system(s) in the present invention. Accordingly, severalportions of sub-system 102 will now be discussed in the followingparagraphs.

Sub-system 102 may be a laptop computer, tablet computer, netbookcomputer, personal computer (PC), a desktop computer, a personal digitalassistant (PDA), a smart phone, or any programmable electronic devicecapable of communicating with the client sub-systems via network 114.Program 300 is a collection of machine readable instructions and/or datathat is used to create, manage and control certain software functionsthat will be discussed in detail, below, in the Example Embodimentsub-section of this Detailed Description section.

Sub-system 102 is capable of communicating with other computersub-systems via network 114. Network 114 can be, for example, a localarea network (LAN), a wide area network (WAN) such as the Internet, or acombination of the two, and can include wired, wireless, or fiber opticconnections. In general, network 114 can be any combination ofconnections and protocols that will support communications betweenserver and client sub-systems.

Sub-system 102 is shown as a block diagram with many double arrows.These double arrows (no separate reference numerals) represent acommunications fabric, which provides communications between variouscomponents of sub-system 102. This communications fabric can beimplemented with any architecture designed for passing data and/orcontrol information between processors (such as microprocessors,communications and network processors, etc.), system memory, peripheraldevices, and any other hardware components within a system. For example,the communications fabric can be implemented, at least in part, with oneor more buses.

Memory 208 and persistent storage 210 are computer-readable storagemedia. In general, memory 208 can include any suitable volatile ornon-volatile computer-readable storage media. It is further noted that,now and/or in the near future: (i) external device(s) 214 may be able tosupply, some or all, memory for sub-system 102; and/or (ii) devicesexternal to sub-system 102 may be able to provide memory for sub-system102.

Program 300 is stored in persistent storage 210 for access and/orexecution by one or more of the respective computer processors 204,usually through one or more memories of memory 208. Persistent storage210: (i) is at least more persistent than a signal in transit; (ii)stores the program (including its soft logic and/or data), on a tangiblemedium (such as magnetic or optical domains); and (iii) is substantiallyless persistent than permanent storage. Alternatively, data storage maybe more persistent and/or permanent than the type of storage provided bypersistent storage 210.

Program 300 may include both machine readable and performableinstructions and/or substantive data (that is, the type of data storedin a database). In this particular embodiment, persistent storage 210includes a magnetic hard disk drive. To name some possible variations,persistent storage 210 may include a solid state hard drive, asemiconductor storage device, read-only memory (ROM), erasableprogrammable read-only memory (EPROM), flash memory, or any othercomputer-readable storage media that is capable of storing programinstructions or digital information.

The media used by persistent storage 210 may also be removable. Forexample, a removable hard drive may be used for persistent storage 210.Other examples include optical and magnetic disks, thumb drives, andsmart cards that are inserted into a drive for transfer onto anothercomputer-readable storage medium that is also part of persistent storage210.

Communications unit 202, in these examples, provides for communicationswith other data processing systems or devices external to sub-system102. In these examples, communications unit 202 includes one or morenetwork interface cards. Communications unit 202 may providecommunications through the use of either or both physical and wirelesscommunications links. Any software modules discussed herein may bedownloaded to a persistent storage device (such as persistent storagedevice 210) through a communications unit (such as communications unit202).

I/O interface set 206 allows for input and output of data with otherdevices that may be connected locally in data communication with servercomputer 200. For example, I/O interface set 206 provides a connectionto external device set 214. External device set 214 will typicallyinclude devices such as a keyboard, keypad, a touch screen, and/or someother suitable input device. External device set 214 can also includeportable computer-readable storage media such as, for example, thumbdrives, portable optical or magnetic disks, and memory cards. Softwareand data used to practice embodiments of the present invention, forexample, program 300, can be stored on such portable computer-readablestorage media. In these embodiments the relevant software may (or maynot) be loaded, in whole or in part, onto persistent storage device 210via I/O interface set 206. I/O interface set 206 also connects in datacommunication with display device 212.

Display device 212 provides a mechanism to display data to a user andmay be, for example, a computer monitor or a smart phone display screen.

The programs described herein are identified based upon the applicationfor which they are implemented in a specific embodiment of theinvention. However, it should be appreciated that any particular programnomenclature herein is used merely for convenience, and thus theinvention should not be limited to use solely in any specificapplication identified and/or implied by such nomenclature.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the invention.The terminology used herein was chosen to best explain the principles ofthe embodiment, the practical application or technical improvement overtechnologies found in the marketplace, or to enable others of ordinaryskill in the art to understand the embodiments disclosed herein.

II. Example Embodiment

FIG. 2 shows flowchart 250 depicting a method according to the presentinvention. FIG. 3A shows program 300 for performing at least some of themethod steps of flowchart 250. This method and associated software willnow be discussed, over the course of the following paragraphs, withextensive reference to FIG. 2 (for the method step blocks) and FIG. 3A(for the software blocks).

Method 250 is adapted to use personalized information pertaining to auser to determine how familiar (or “novel” or “surprising”) a newartifact will be to the user. As used herein, an artifact may be anyitem (whether existing conceptually or in physical space) that is ableto be identified by the user. In the present example embodiment, to bediscussed throughout this sub-section, artifacts include televisionshows. Other examples of artifacts are discussed below in the FurtherComments and/or Embodiments sub-section of this Detailed Description.

Processing begins at step S255, where data collection module (“mod”) 302(see FIG. 3A) receives a dataset pertaining to the user. The dataset maybe received in a variety of ways including, for example, over a computernetwork. Additionally, the data in the dataset may come in a widevariety of forms. For example, the data may include textual content,photographs, videos, and/or any other known (or yet to be known) type ofmultimedia content.

In many embodiments of the present invention, the dataset pertaining tothe user includes some amount of personalized information for the user.Generally speaking, personalized information includes any informationthat is likely to have been created by, or viewed by, the user. Forexample, personalized information can include the user's activity on asocial networking service (SNS), activity the user can view on a SNS,the user's online purchase history, and/or the user's browser history(for additional discussion of social networking services andpersonalized information, see the Definitions sub-section of thisDetailed Description).

The amount of personalized information contained in the dataset may varybetween embodiments. In some embodiments, a single piece of personalizedinformation is included in the dataset. However, in other embodiments, acertain personalized information threshold must be met in order for thedataset to be received (in one embodiment, for example, 80% of theinformation in the dataset must be personalized information).Additionally, it should be noted that information other thanpersonalized information may be helpful in determining the user'sfamiliarity of an unknown artifact. For example, in some embodiments,the dataset may include general, well-known knowledge that the user isassumed (but not confirmed) to know (such as, for example, informationrelating to personalized information in the dataset). The dataset mayalso include personalized information for social networking connectionsof the user (that is, the dataset may include information that ispersonal for the user's connections, as opposed to the user).Furthermore, the dataset may also include personalized information forsocial networking connections of the social networking connections ofthe user (or “second degree” social networking connections), and so on(for more examples of social networking connections, see the Definitionssub-section of this Detailed Description).

In the present example embodiment, the dataset includes informationabout two television shows: a science fiction show and a detective show.For personalized information, the dataset includes the user's SNSpostings about the two shows and the user's online purchase history ofepisodes of the two shows. Additionally, the dataset includesnon-personalized information relating to the shows, includinginformation about the actors and the companies that produce the shows.

Processing proceeds to step S260 (see FIG. 2), where artifactidentification mod 304 (see FIG. 3A) uses the dataset to build acollection of artifacts known to the user. The collection of artifactsis stored in artifact data store 310. The collection of artifacts may bestructured and/or built in a wide variety of ways so as to model theuser's knowledge of the artifacts. For example, in many embodiments, theartifacts contain various characteristics, components, and/or categories(generally referred to as “characteristics”). The characteristics helpmethod 250 determine similarities/differences between artifacts andultimately determining the probabilistic familiarity of the unknownartifact (to be discussed in later steps). In the present exampleembodiment, the television show information is structured such that theartifacts (television shows) include the following characteristics:actors, production companies, and themes. An example representation ofthe collection of artifacts 312 a stored in artifact data store 310 a isshown in FIG. 3B. As shown in FIG. 3B, the science fiction show has thefollowing characteristics: (i) it stars actors “Actor A”, “Actor B”, and“Actor C” (shown in actors data store 314 a); (ii) it was produced by“BCD Company” (shown in production companies data store 316 a); and(iii) it has themes of “Mystery” and “Space Exploration” (shown inthemes data store 318 a). Additionally, the detective show: (i) starsactors “Actor C” and “Actor D” (shown in actors data store 314 a); (ii)was produced by “XYZ Company” (shown in production companies data store316 a); and (iii) has themes of “Mystery” and “Buddy Cop” (shown inthemes data store 318 a).

Processing proceeds to step S265, where artifact identification mod 304(see FIG. 3A) receives the identity of an artifact that is unknown tothe user (or “unknown artifact”). The unknown artifact may be receivedfrom a wide variety of possible sources. For example, in someembodiments, the unknown artifact is an existing artifact beingrecommended to the user. In other embodiments, the unknown artifact maybe an entirely novel artifact produced using a method for creating novelartifacts. In the present example embodiment, the unknown artifact is anexisting television show (a situation comedy).

Processing proceeds to step S270, where probabilistic familiarity mod306 (see FIG. 3A) generates a probabilistic familiarity value (“PFV”)for the unknown artifact with respect to the collection of knownartifacts by applying a probabilistic familiarity algorithm. Generallyspeaking, the probabilistic familiarity algorithm may include one ormore of a number of known probability-based methods for calculatingfamiliarity (or “novelty” or “surprise”). In many embodiments, aBayesian surprise-based algorithm is used (for a further discussion ofprobabilistic familiarity and Bayesian surprise-based algorithms, seethe Further Comments and/or Embodiments sub-section of this DetailedDescription).

In the present example embodiment, the PFV is generated by first addingthe unknown artifact to the artifact data store and incorporating itinto the existing collection of artifacts. FIG. 3C shows a portion ofartifact data store 310 b, indicating the characteristics for theunknown artifact (including existing characteristics of known artifactsand new characteristics unique to the unknown artifact). Specifically,the unknown artifact (i.e. the situation comedy show), which is shown innew artifact store 312 b: (i) stars actors “Actor E” and “Actor F”(shown in actors data store 314 b); (ii) was produced by “XYZ Company”(shown in production companies data store 316 b); and (iii) has themesof “Sibling Rivalry” and “Slapstick Humor” (shown in themes data store318 b). Based on this information, probabilistic familiarity mod 306determines that the situation comedy show has a probabilisticfamiliarity value of 1 (indicating a low level of familiarity due to thelarge differences between the cast and themes of the situation comedyshow and the known shows).

Processing proceeds to step S275, where UI mod 308 (see FIG. 3A) reportsthe PFV to the user. In the present example embodiment, theprobabilistic familiarity value is displayed on a screen as part of atelevision show recommendation engine. Based on the score, therecommendation engine informs the user that the user will be likely tofind the situation comedy show to be novel and surprising, due to a lackof familiarity with the show's known characteristics. That being said,in other embodiments, the PFV may not be displayed to the user at all,or may be displayed only if the PFV is below a probabilistic familiaritythreshold. In some embodiments, for example, if the PFV is below theprobabilistic familiarity threshold, the artifact is added to a list ofartifacts that are not familiar to the user. UI mod 308 may thendetermine whether to display the PFV to the user based on a set ofdisplay conditions.

III. Further Comments and/or Embodiments

Some embodiments of the present invention recognize the following facts,potential problems, and/or potential areas for improvement with respectto the current state of the art as relates to novelty and/or surprise:(i) existing solutions do not use Bayesian surprise to quantify thenovelty of a product or personalize a novelty assessment; (ii) someexisting solutions focus on the decay of novelty; (iii) existingsolutions do not use social networks, shared contents, or purchasehistory to create a personalized set of artifacts known to a given user;(iv) many known solutions do not define components and artifacts in anontology; and/or (v) known solutions that recommend existing products donot evaluate the novelty of product candidates that haven't been madeyet.

Some embodiments of the present invention may include one, or more, ofthe following features, characteristics, and/or advantages: (i) creatinga personalized novelty assessment specific to a targeted observer and/ora targeted social group; (ii) measuring the change in an observer'sbelief of known artifacts after observing a newly created artifact(where the larger the change is, the more surprising the newly createdartifact is); (iii) characterizing an observer's belief by theprobability distribution of artifacts; (iv) incorporating temporalproximity as a measure of belief, as artifacts may be forgotten overtime; (v) incorporating social proximity as a measure of belief, as anartifact known to a member of an observer's social network is morelikely to be known by the observer; (vi) using ontologies to defineartifacts, artifact components, and data sources; and/or (vii) extendingontologies beyond typical domains for a personalized novelty assessment.

Some embodiments of the present invention use social networks, sharedcontents, purchase history, and internet activity history to create apersonalized set of artifacts (and artifact components) known to a givenuser. FIG. 4 shows flowchart 400 depicting a method according to thepresent invention, and FIG. 5 shows program 500 for performing at leastsome of the method steps of flowchart 400. This method and associatedsoftware will now be discussed, over the course of the followingparagraphs, with reference to FIG. 4 (for the method step blocks) andFIG. 5 (for the software blocks).

Processing begins at step S405, where social network analyzer 505 (seeFIG. 5) identifies social connections, and artifact analyzer 510 (seeFIG. 5) retrieves information from the social connections to define anontology of artifacts and their components. In this step: (i) a domainis defined (such as food); (ii) information about known artifacts andcomponents is collected from various data sources; and (iii) the knownartifacts are decomposed into components. The result of step S405 is anontology of artifacts and components that is personalized for thespecific user.

Social network analyzer 505 is adapted to identify the user's socialnetwork connections. Social network connections may include, forexample, anyone who is connected to the user via an Internet-basedsocial networking service. Additionally, network connections may includesecond degree connections (connections of existing connections) andthird degree connections (connections of second degree connections).However, this is not meant to be limiting, and social network analyzer505 may be adapted to identify social connections from a wide variety ofsources and in a wide variety of ways.

Once connections have been identified, artifact analyzer 510 (see FIG.5) scans the identified connections for known artifacts and artifactcomponents. Additionally, artifact analyzer 510 may scan informationfrom other available data sources, such as the user's search history,websites the user has visited, and purchases the user has made.Information from a particular data source may be used on its own or incombination with information from other data sources to identifyartifacts and artifact components. Some examples of possible scanningmethods include: (i) text parsing, to identify names of artifacts,components, and categories from text; (ii) image recognition, toidentify artifacts, components, and categories from pictures; and/or(iii) web-crawling, to analyze network connections.

Diagram 600 (see FIG. 6) shows some examples of artifacts (andcomponents) in the “food” domain that are retrievable from socialnetworks. Referring to FIG. 6: (i) row 610 shows various types of socialnetworks; (ii) row 620 shows types of information that can be retrievedfrom those social networks; and (iii) row 630 shows artifacts, artifactcomponents, and other useful items that can be extracted from thatinformation. For example, with a location-based social network, artifactanalyzer 510 can generally retrieve information about the restaurantsvisited, restaurant menus, and friends of the user. This information canbe translated into: (i) artifacts, such as recipes; (ii) artifactcomponents, such as ingredients; and (iii) user social networkconnections, where the connections themselves may include additionalrecipes and ingredients.

An example of information contained in a recipe-based social network isshown in screenshot 700 (see FIG. 7). In this example, the recipe-basedsocial network includes recipes (with corresponding ingredients) as wellas user comments about the recipes. Selected area 702, which includesthe text “I recommend adding garlic and red bell peppers for extraflavor,” shows a specific user comment that can be used in creatingartifacts and components. Specifically, this comment (which was made bythe user) shows that the user has been exposed to both garlic and redbell peppers. Additionally, it provides information about the user'spreferences; based on the comment, artifact analyzer 510 is able toconclude that the user enjoys garlic and red bell peppers, and that theuser has created his own recipe with those ingredients. In this case,the user is acting as an agent of innovation by not only viewing andusing artifacts, but modifying them in ways that matter to the user.

Once artifacts and components have been identified, belief analyzer 515(see FIG. 5) uses those artifacts and components to create ontologies.In many embodiments, ontologies will be created at both the artifactlevel and the component level, in order to identify theartifact/component traits needed to properly calculate novelty and/orsurprise (discussed below). Furthermore, for similar reasons, multipleontologies may be created for each artifact/component type.

Some ontologies according to an example embodiment of the presentembodiment are shown in FIG. 8A, FIG. 8B, FIG. 8C, FIG. 9A, FIG. 9B,FIG. 9C, and FIG. 9D. FIGS. 8A, 8B, and 8C depict ontologies of artifactcomponents (in this case, ingredients), while FIGS. 9A, 9B, 9C, and 9Ddepict ontologies of artifacts (in this case, recipes). Specifically,with regard to ingredient ontologies: (i) diagram 810 (see FIG. 8A)depicts an ontology of ingredients organized by ingredient type; (ii)diagram 820 (see FIG. 8B) depicts an ontology of ingredients organizedby cuisine type; and (iii) diagram 830 (see FIG. 8C) shows a visualrepresentation of the ontologies of FIGS. 8A and 8B, with ingredientsgrouped based on their shared characteristics. Regarding recipeontologies: (i) diagram 910 (see FIG. 9A) depicts an ontology of recipesorganized by dish type (meat or cheese); (ii) diagram 920 (see FIG. 9B)depicts an ontology of recipes organized by dish type (pizza or pasta);(iii) diagram 930 (see FIG. 9C) depicts an ontology of recipes organizedby cuisine (for example, regions the recipes are typically associatedwith); and (iv) diagram 940 (see FIG. 9D) shows a visual representationof the ontologies of FIGS. 9A, 9B, and 9C, with recipes grouped based ontheir shared characteristics.

Processing then proceeds to step S410 (see FIG. 4), where beliefanalyzer 515 (see FIG. 5) assigns a familiarity score (also referred toas a prior probability score/distribution) to each known artifact.Belief analyzer 515 may use a wide variety of methods to do this. Method1000 (shown in FIG. 10) is an example of one such method for determiningprior probability distributions. Processing begins at step S1002, wheremethod 1000 identifies an artifact (or component) A, a person P (theperson who observed the artifact), and a time T (the time at whichperson P observed the artifact).

Processing proceeds to steps S1004, S1006, and S1008 (see FIG. 10). Instep S1004, method 1000 determines all persons (P′) in the neighborhood(or social network) of P. In step S1006, method 1000 determines allartifacts (A′) in the neighborhood of A (based on artifact categories).In step S1008, method 1000 determines all time points (T′) prior to thetime T that artifact A was observed.

Processing proceeds to step S1010, where method 1000 counts thefrequency for all values of A′, P′, and T′ (represented as f(A′, P′,T′)). Then, processing proceeds to step S1012, where the sum of thevalues of f(A′, P′, T′) is weighted by a factor inversely related to thecloseness of A, P, and T.

The closeness to A (or the “ontology closeness”) may be determined in awide variety of ways. In some embodiments, the closeness to A (that is,the distance between A′ and A) is determined by using the followingformula:

d(A,A′)=l(A)+l(A′)−2l(LCA(A,A′))

In these embodiments, “d” represents the distance, “l” is the nodelevel, and “LCA” is the lowest common ancestor.

The closeness to P (or the “social proximity measure”) may also bedetermined in a wide variety of ways. In some embodiments of the presentinvention, the social proximity measure may be represented by theminimum path length between P and P′ in a social network graph. In otherembodiments, the social proximity measure may equal the stationaryprobability of traversing from P to P′ (or vice versa) through a randomwalk on a social network graph.

The closeness to T (or the “temporal proximity measure”) may also bedetermined in a wide variety of ways. In some embodiments, the temporalproximity measure (which is a function of time) may be represented bythe following equation:

$R = ^{- \frac{t}{S}}$

In these embodiments, “R” represents memory retention, “S” is therelative strength of a person's memory, and “t” is time.

In many embodiments of the present invention, once the ontologycloseness, social proximity measure, and temporal proximity measure havebeen determined, method 1000 (see FIG. 10) uses them to create aweighted frequency ({tilde over (f)}) of artifact A to person P at timeT. In one embodiment, the weighted frequency is represented by thefollowing equation:

${\overset{\sim}{f}\left( {A,P,T} \right)} = {\sum\limits_{t^{\prime} \leq T}\; {{w_{T}\left( {T^{\prime},T} \right)} \times \begin{Bmatrix}{\sum\limits_{p^{\prime} \in {{neighborhood}\mspace{14mu} {of}\mspace{14mu} P}}\; {{w_{S}\left( {P^{\prime},P} \right)} \times}} \\\left\lbrack {\sum\limits_{a^{\prime} \in {{neighborhood}\mspace{14mu} {of}\mspace{14mu} A}}\; {{w_{O}\left( {A^{\prime},A} \right)} \times {f\left( {A^{\prime},P^{\prime},T^{\prime}} \right)}}} \right\rbrack\end{Bmatrix}}}$

In this embodiment: (i) w_(O) represents the weight inversely related toontology closeness; (ii) w_(S) represents the weight inversely relatedto social proximity; and (iii) w_(T) represents the weight inverselyrelated to temporal proximity. The weighted frequency equation is usedfor all known artifacts, thereby producing a familiarity score (or priorprobability distribution) for each known artifact. Additionally, ifdesired: (i) a small pseudo count can be assigned to each unseenartifact composable by known artifact components; and (ii) the weightedfrequencies can be normalized by taking a sum of the weightedfrequencies.

Once a familiarity score has been generated for each artifact,processing then proceeds to step S415 (see FIG. 4), where beliefanalyzer 515 computes a personalized surprise score for a new existingor novel artifact. To compute the personalized surprise score, beliefanalyzer must first calculate the posterior probability distribution forthe new artifact. The posterior probability distribution may be computedin a number of ways. For example, in one embodiment, the posteriorprobability distribution is calculated by adding the new artifact to theknown artifacts and calculating the artifact's prior probabilitydistribution using method 1000 (discussed above).

In some embodiments of the present invention, a Bayesian method (seeBackground) is used to calculate the posterior probability distribution.For example, in one embodiment, the following algorithm is used:

${P\left( M \middle| A \right)} = {\frac{P\left( A \middle| M \right)}{P(A)}{P(M)}}$

In this example, “P” is a probability distribution, “M” is the set ofartifacts known to the observer, and “A” is the new artifact beingobserved.

Once the posterior probability distribution has been determined, thepersonalized surprise score can be computed. The personalized surprisescore may be computed in a number of ways. In the Bayesian examplediscussed above, the following algorithm may be used:

${S\left( {A,M} \right)} = {{D_{KL}\left( {P\left( M \middle| A \right)}||{P(M)} \right)} = {\Sigma_{A}{P_{2}(A)}\log \frac{P_{2}(A)}{P_{1}(A)}}}$

In this example: (i) “S” is the surprise score; (ii) “A” is the newartifact being observed; (iii) “M” is the set of artifacts known to theobserver; (iv) “P” is a probability distribution; (v) D_(KL) is aKullback-Leibler divergence (see Background); (vi) P₂(A) is theposterior probability of A; and (vii) P₁(A) is the prior probability ofA.

To provide an example, in one embodiment, a user's existing recipe(artifact) repository includes two recipes: (a, b, c) and (b, c, d),where a, b, c, and d are ingredients (components). In this example, apersonalized surprise score for new recipe (a, b, e) is beingdetermined. The following table shows the prior probability andposterior probability for each ingredient (where “ε” represents theweight of unseen ingredient “e” in the existing recipe repository):

Ingredient (single) a b c d e Prior 1/(6 + ε) 2/(6 + ε) 2/(6 + ε) 1/(6 +ε) ε/(6 + ε) probability Posterior 2/9 3/9 2/9 1/9 1/9 probability

To determine the personalized surprise score for recipe (a, b, e),method 400 simply takes these prior probability and posteriorprobability values and calculates the Kullback-Leibler distance usingthe following formula (mentioned above):

${\Sigma_{A}{P_{2}(A)}\log \frac{P_{2}(A)}{P_{1}(A)}},$

where “A” is the set of ingredients {a,b,c,d,e}.

Once the personalized surprise score is computed, processing for method400 completes. In some embodiments of the present invention, thesurprise score is calculated for an existing artifact/component that isnot known to the user. However, in other embodiments, existingcomponents may be combined in new ways to form new artifacts, and thesurprise score of the resulting artifacts may be used for the purpose ofnew product creation for targeted markets and/or demographics, forexample.

IV. Definitions

Present invention: should not be taken as an absolute indication thatthe subject matter described by the term “present invention” is coveredby either the claims as they are filed, or by the claims that mayeventually issue after patent prosecution; while the term “presentinvention” is used to help the reader to get a general feel for whichdisclosures herein are believed to potentially be new, thisunderstanding, as indicated by use of the term “present invention,” istentative and provisional and subject to change over the course ofpatent prosecution as relevant information is developed, and as theclaims are potentially amended.

Embodiment: see definition of “present invention” above—similar cautionsapply to the term “embodiment.”

and/or: inclusive or; for example, A, B “and/or” C means that at leastone of A or B or C is true and applicable.

User/subscriber: includes, but is not necessarily limited to, thefollowing: (i) a single individual human; (ii) an artificialintelligence entity with sufficient intelligence to act as a user orsubscriber; and/or (iii) a group of related users or subscribers.

Module/Sub-Module: any set of hardware, firmware and/or software thatoperatively works to do some kind of function, without regard to whetherthe module is: (i) in a single local proximity; (ii) distributed over awide area; (iii) in a single proximity within a larger piece of softwarecode; (iv) located within a single piece of software code; (v) locatedin a single storage device, memory or medium; (vi) mechanicallyconnected; (vii) electrically connected; and/or (viii) connected in datacommunication.

Computer: any device with significant data processing and/or machinereadable instruction reading capabilities including, but not limited to:desktop computers, mainframe computers, laptop computers,field-programmable gate array (FPGA) based devices, smart phones,personal digital assistants (PDAs), body-mounted or inserted computers,embedded device style computers, application-specific integrated circuit(ASIC) based devices.

Social Networking Service (SNS): any digital platform by whichindividuals or groups share, create, discuss, and/or modify content.Examples of SNS types include, but are not limited to, the following:(i) location-based; (ii) collection and/or bookmarking-based; (iii)food-based; (iv) recipe-based; (v) photography-based; (vi) chat-based;(vii) dating-based; (viii) order/delivery-based; (ix) company-based; (x)industry-based; (xi) interest-based; and/or (xii) general purpose.

Social Networking Connection: any individual or group connected to auser via a social networking service. Connections are sometimes alsoreferred to as “friends,” “circles”, “followers”, “contacts”, and thelike.

Personalized information: any information likely to have been created byor viewed by a particular user. Examples of personalized data include,but are not limited to: (i) activity by the user on a SNS; (ii) activityviewable by the user on a SNS; (iii) online purchase history of theuser; (iv) browser history of the user; (v) blog posts by the user; (vi)website content created by and/or maintained by the user; (vi) useremail content; (vii) user online chat content; and/or (viii) contentcreated by a third party but attributing portions (such as quotations)to the user.

What is claimed is:
 1. A method comprising: receiving the identity of afirst user; receiving a first dataset pertaining to the first user;receiving the identity of a first artifact; and applying a probabilisticfamiliarity algorithm to the first dataset with respect to the firstartifact to yield a probabilistic familiarity value for the firstartifact with respect to the first user; wherein: the first dataset isreceived over a computer network; and the first dataset includes atleast one piece of personalized information for the first user.
 2. Themethod of claim 1, further comprising: determining if the probabilisticfamiliarity value is below a first probabilistic familiarity threshold;wherein if the probabilistic familiarity value is below the firstprobabilistic familiarity threshold, the first artifact is added to aset of artifact(s) that are not familiar to the first user.
 3. Themethod of claim 1, wherein: the personalized information includes atleast one of the following: activity by the user on a social networkingservice (SNS); activity viewable by the user on a SNS; online purchasehistory of the user; online browser history of the user; blog posts bythe user; website content created by and/or maintained by the user; useremail content; user online chat content; and/or content created by athird party but attributing portions to the user.
 4. The method of claim1, wherein: the probabilistic familiarity algorithm is a Bayesiansurprise-based algorithm.
 5. The method of claim 1, wherein: the firstdataset further includes at least one piece of personalized informationfor a first social networking connection of the first user, where theuser is connected to the first social networking connection on a firstsocial networking service.
 6. The method of claim 5, wherein: the firstdataset further includes at least one piece of personalized informationfor a second social networking connection, where the second socialnetworking connection is connected to the first social networkingconnection on the first social networking service.
 7. A computer programproduct comprising a computer readable storage medium having storedthereon: first program instructions programmed to receive the identityof a first user; second program instructions programmed to receive afirst dataset pertaining to the first user; third program instructionsprogrammed to receive the identity of a first artifact; and fourthprogram instructions programmed to apply a probabilistic familiarityalgorithm to the first dataset with respect to the first artifact toyield a probabilistic familiarity value for the first artifact withrespect to the first user; wherein: the first dataset is received over acomputer network; and the first dataset includes at least one piece ofpersonalized information for the first user.
 8. The computer programproduct of claim 7, further comprising: fifth program instructionsprogrammed to determine if the probabilistic familiarity value is belowa first probabilistic familiarity threshold; wherein if theprobabilistic familiarity value is below the first probabilisticfamiliarity threshold, the first artifact is added to a set ofartifact(s) that are not familiar to the first user.
 9. The computerprogram product of claim 7, wherein: the personalized informationincludes at least one of the following: activity by the user on a socialnetworking service (SNS), activity viewable by the user on a SNS, onlinepurchase history of the user, online browser history of the user, blogposts by the user, website content created by and/or maintained by theuser, user email content, user online chat content, and/or contentcreated by a third party but attributing portions to the user.
 10. Thecomputer program product of claim 7, wherein: the probabilisticfamiliarity algorithm is a Bayesian surprise-based algorithm.
 11. Thecomputer program product of claim 7, wherein: the first dataset furtherincludes at least one piece of personalized information for a firstsocial networking connection of the first user, where the user isconnected to the first social networking connection on a first socialnetworking service.
 12. The computer program product of claim 11,wherein: the first dataset further includes at least one piece ofpersonalized information for a second social networking connection,where the second social networking connection is connected to the firstsocial networking connection on the first social networking service. 13.A computer system comprising: a processor(s) set; and a computerreadable storage medium; wherein: the processor set is structured,located, connected and/or programmed to run program instructions storedon the computer readable storage medium; and the program instructionsinclude: first program instructions programmed to receive the identityof a first user; second program instructions programmed to receive afirst dataset pertaining to the first user; third program instructionsprogrammed to receive the identity of a first artifact; and fourthprogram instructions programmed to apply a probabilistic familiarityalgorithm to the first dataset with respect to the first artifact toyield a probabilistic familiarity value for the first artifact withrespect to the first user; wherein: the first dataset is received over acomputer network; and the first dataset includes at least one piece ofpersonalized information for the first user.
 14. The computer system ofclaim 13, further comprising: fifth program instructions programmed todetermine if the probabilistic familiarity value is below a firstprobabilistic familiarity threshold; wherein if the probabilisticfamiliarity value is below the first probabilistic familiaritythreshold, the first artifact is added to a set of artifact(s) that arenot familiar to the first user.
 15. The computer system of claim 13,wherein: the personalized information includes at least one of thefollowing: activity by the user on a social networking service (SNS),activity viewable by the user on a SNS, online purchase history of theuser, online browser history of the user, blog posts by the user,website content created by and/or maintained by the user, user emailcontent, user online chat content, and/or content created by a thirdparty but attributing portions to the user.
 16. The computer system ofclaim 13, wherein: the probabilistic familiarity algorithm is a Bayesiansurprise-based algorithm.
 17. The computer system of claim 13, wherein:the first dataset further includes at least one piece of personalizedinformation for a first social networking connection of the first user,where the user is connected to the first social networking connection ona first social networking service.
 18. The computer system of claim 17,wherein: the first dataset further includes at least one piece ofpersonalized information for a second social networking connection,where the second social networking connection is connected to the firstsocial networking connection on the first social networking service.